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Applying Macroclimatic Variables to Improve Flow Rate Forecasting Using Neural Networks Techniques

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

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Abstract

Since 2013, the São Francisco River has being through a low hydraulicity period. In other words, the rain intensity is below average. Consequently, it has being necessary to operate at a minimal flow rate. It is far below the ones established at the operation licence, which is 1300 m\(^3\)/s. Due to this hydraulic crisis, the actual operational flow rate is 700 m\(^3\)/s at São Francisco River, characterizing this situation as critical. In this work, it was proposed to use Reservoir Computing (RC), Long Short Term Memory (LSTM) and Deep Learning to predict Sobradinho’s flow rate for 1, 2 and 3 months ahead using macroclimatic variables. After having the results for each one of them, a comparison was made and statistical tests where executed for evaluation.

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References

  1. Valença, M.J.S., Ludermir, T.B., Valença, A., Vasconcelos, I.: Sistema de Apoio a Decisão para a Operação Hidráulica de Sobradinho Incorporando Tendências Macro-Climáticas Utilizando Redes Neurais (2009)

    Google Scholar 

  2. Brooks, K.N., Ffolliott, P.F., Magner, J.A.: Hydrology and the Management of Watersheds, 3 edn. (2003)

    Google Scholar 

  3. Valença, M.J.S.: Proposta de Alerta Ambiental baseada na Previsão de Volumes Máximos Afluentes ao reservatório de Sobradinho

    Google Scholar 

  4. Agência Nacional de Águas (ANA): http://www2.ana.gov.br/Paginas/servicos/saladesituacao/v2/saofrancisco.aspx/

  5. What is Air Temperature? https://www.fondriest.com/news/airtemperature.htm

  6. Mendonça, F., Dubreuil, V.: Termografiada superfície e temperatura do ar na rmc (regiãometropolitana de curitiba/pr). Editora UFPR, pp. 25–35 (2005)

    Google Scholar 

  7. Cavalcanti, E.P., de Silva, V.P.R., de Sousa, A.S.F.: Programa computacional para a estimativa da temperatura do ar para a região nordeste do Brazil. Revista Brasileira de Engenharia Agrícola e Ambiental, pp. 140–147 (2006)

    Google Scholar 

  8. Embrapa: http://www.agencia.cnptia.embrapa.br/Agencia22/AG01/arvore/AG01_79_24112005115223.html

  9. Bomventi, T.N., Wainer, I.E.K.C., Taschetto, A.S.: Relação entre a radiação de onda longa, precipitação e temperatura da superfície do mar no oceano atlântico tropical. Revista Brasileira de Geofísica 24, 513–524, 12 2006. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-261X2006000400005&nrm=iso

    Article  Google Scholar 

  10. Susskind, J., Molnar, G., Iredell, L.: Contributions to Climate Research Using the AIRS Science Team Version-5 Products (2011)

    Google Scholar 

  11. de Stefano Ermenegildo, L.F., Pereira, S.B., Arai, F.K., Rosa, D.B.C.J.: Vazão específica e precipitação média na bacia do ivinhema. Dourados, pp. 428–432 (2012)

    Google Scholar 

  12. Tucci, C.E.M.: Hidrologia: ciência e aplicação. ABRH, 3 edn. (2002)

    Google Scholar 

  13. Rodrigues, A.L.: Informações macroclimáticas aplicadas na previsão de vazões. São Paulo (2016)

    Google Scholar 

  14. Valença, M.J.S.: Fundamentos das Redes Neurais. Livro Rápido, Brazil (2016)

    Google Scholar 

  15. Ye Rena, P.N., Suganthana, N.S., Amaratungac, G.: Random vector functional link network for short-term electricity load demand forecasting. Inf. Sci. 367–368, 1078–1093 (2016)

    Article  Google Scholar 

  16. Ferreira, A.A., Ludermir, T.B.: Um Método para Design e Treinamento de Reservoir Computing Aplicado à Previsão de Séries Temporais. Pernambuco (2011)

    Google Scholar 

  17. Jaeger, H.: The “echo state” approach to analyzing and training recurrent neural networks (2011)

    Google Scholar 

  18. Mass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002)

    Article  Google Scholar 

  19. Verstraeten, D.: Reservoir computing: computation with dynamical systems. Belgium (2009)

    Google Scholar 

  20. A Gentle Introduction to Backpropagation Through Time. https://machinelearningmastery.com/gentle-introduction-backpropagation-time/

  21. Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras. https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/

  22. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 1735–1780 (1997)

    Article  Google Scholar 

  23. Deng, L., Yu, D.: Deep Learning Methods and Applications, Foundations and Trends in Signal Processing, 7th edn. Now Publishers Inc., Boston (2014)

    Google Scholar 

  24. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

    MATH  Google Scholar 

  25. National Oceanic and Atmospheric Administration (NOAA). http://www.noaa.gov/

  26. Eletrobras. http://eletrobras.com/pt/Paginas/home.aspx

  27. Companhia Hidroelétrica do São Francisco (Chesf). http://www.chesf.gov.br

  28. Wang, L., Fu, X.: Data Mining with Computational Intelligence. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-28803-1

    Book  MATH  Google Scholar 

  29. Hartshorn, S.: Linear Regression And Correlation: A Beginner’s Guide, 1st edn. Amazon Digital Services LLC, Seattle (2017)

    Google Scholar 

  30. Hartshorn, S.: Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners, 1st edn. Amazon Digital Services LLC, Seattle (2016)

    Google Scholar 

  31. Kok Keong Teo, L.W., Lin, Z.: Wavelet packet multi-layer perceptron for chaotic time series prediction: effects of weight initialization. In: International Conference on Computational Science, vol. 2074, pp. 310–317, 7 2001

    Google Scholar 

  32. Keras. https://keras.io/

  33. Python. https://www.python.org/

  34. Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations (2014)

    Google Scholar 

  35. A simple deep learning model for stock price prediction using TensorFlow. https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877

  36. Tensorflow. https://www.tensorflow.org/

  37. Moreno, A.M., Juristo, N.: Basics of Software Engineering Experimentation. Kluwer Academic Publisher, Dordrecht (2001)

    MATH  Google Scholar 

  38. Venables, W.N., Smith, D.M., the R Core Team: An Introduction to R. Network Theory Ltd, Hershey (2009)

    Google Scholar 

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Correspondence to Breno Santos .

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Santos, B., Aguiar, B., Valença, M. (2018). Applying Macroclimatic Variables to Improve Flow Rate Forecasting Using Neural Networks Techniques. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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